Blind Modulation Recognition of Communication Signals Based on Support Vector Machines
Subject Areas : electrical and computer engineering
S.
Shaerbaf
1
(Ferdosi University)
M.
Khademi
2
(Ferdowsi University of Mashhad)
Mohammad
Molavi
3
(Ferdosi University)
Keywords: Genetic algorithmsupport vector machinemodulation recognitionpattern recognition,
Abstract :
Automatic modulation type classifier is a system which recognizes the modulation type of received signal automatically from some possible, pre-assumed types. Automatic modulation classification has applications such as spectrum surveillance, signal confirmation, interference identification, software radio, etc. This paper, proposes a new method for recognition of 9 famous digital and analog modulations, which no need for prior knowledge of the signal to be recognized. This system is used to separate AM, FM, DSB and SSB in Analog modulations and 2ASK, 2PSK, 2FSK, 4PAM and 16QAM in digital modulations. Support Vector Machines (SVM) is used to classify these modulations and Genetic Algorithm is used to optimize Classifier Structure. Simulation results show that proposed algorithms have a good performance in comparison with other algorithms. Computational simplicity, High training speed and High classification rate, are the advantages of proposed algorithms.
[1] Y. Yang and S. S. Soliman, "A suboptimal algorithm for modulation classification," IEEE Trans. on Aerospace and Electronic Systems, vol. 33, no. 1, pp. 38-45, Jan. 1997.
[2] W. Wei and M. Mendel, "A new maximum - likelihood for modulation classification," in Proc. IEEE Int. Conf. onCommunications, vol. 1, pp. 57-61, Feb. 1996.
[3] E. Azzouz and A. Nandi, "Algorithms for automatic modulation recognition of communication signals," IEEE Trans. on Commun., vol. 46, no. 4, pp. 431-436, Jan. 1998.
[4] A. Iversen, Classification of Digital Modulation Schemes Using Multi - Layered Perceptrons, Ph.D Thesis, Heriot Watt University, pp. 30-35, Feb. 2004.
[5] Q. Cai and P. Wei, "A digital modulation recognition method," in Proc. IEEE Int. Conf. on Communications, vol. 3, pp. 863-866, Apr. 2004.
[6] F. Bo, C. Jun, and C. Gnag, "An algorithm of SNR estimation for non constant envelope signal in the AWGN channel," Acta Electronica Sinica, vol. 30, no. 1, pp. 1369-1371, Apr. 2002.
[7] H. Stark and J. W. Woods, Probability, Random Processes, and Estimation Theory for Engineers, 2nd ed., Prentice - Hall, 1994.
[8] C. J. C. Burges, "A tutorial on support vector machines for pattern recognition," Data Mining and Knowledge Discovery, vol. 2, no. 1, pp. 955-974, Apr. 1998.